Image editing applications are often used to generate smaller images through down-scale resampling. For instance, image editing applications have the ability to down-scale images by transforming an input image into an output image with a reduced resolution. Down-scaling operations inherently reduce the amount of information included in the output image as compared to the input image, and are therefore prone to various artifacts, including blur, noise, and aliasing.
Existing down-scale resampling techniques involve a tradeoff between blur and aliasing, which is often considered the least desired artifact as a result of resampling. In one example, nearest neighbor resampling, which is one down-scale resampling technique, produces output images with minimal blurring but with the high aliasing. In another example, As a result, techniques such as bi-linear resampling and bi-cubic resampling produce down-scaled results with less aliasing but more blur (e.g., by pre-filtering the input image before down-scale resampling). Thus, existing methods for image down-scaling fail to generate down-scaled images with reduced blur and aliasing.